Primary exercises
- Dietary intakes. (Create a vector, use it in
calculation.)
Four patients had daily dietary intakes of 2314, 2178, 1922, 2004
kcal.
Make a vector intakesKCal of these four values.
What is the class of this vector?
Convert the values into in kJ using 1 kcal = 4.184 kJ.
intakesKCal <- c( 2314, 2178, 1922, 2004 )
intakesKCal
[1] 2314 2178 1922 2004
class( intakesKCal )
[1] "numeric"
intakesKCal * 4.184
[1] 9681.776 9112.752 8041.648 8384.736
- More dietary intakes. (Combining/appending/merging
vectors.)
Additional set of intakes is provided: 2122, 2616, NA, 1771 kcal.
Use c() to append the new intakes after values in
intakesKCal and store the result in
allIntakesKCal.
Print the combined vector and print its calculated
length.
intakesKCal2 <- c( 2122, 2616, NA, 1771 )
allIntakesKCal <- c( intakesKCal, intakesKCal2 )
allIntakesKCal
[1] 2314 2178 1922 2004 2122 2616 NA 1771
length( allIntakesKCal )
[1] 8
- The average and total intakes. (Calculating means and sums,
skipping missing values.)
Calculate mean intake for patients in vector
intakesKCal.
Next, calculate mean intake for patients in vector
allIntakesKCal.
Can you explain the result?
Check help for ?mean, in particular the na.rm
argument.
Use the extra argument na.rm=TRUE to calculate the
mean of non-NA elements of
allIntakesKCal.
Check help for ?sum how to omit NA elements in
sum calculation.
Now, calculate the total sum of allIntakesKCal
intakes ignoring the NA element.
mean( intakesKCal )
[1] 2104.5
mean( allIntakesKCal )
[1] NA
# since one element is missing, the mean is unknown
# ?mean, adding argument na.rm=TRUE will omit NA elements
mean( allIntakesKCal, na.rm = TRUE )
[1] 2132.429
# ?sum also allows na.rm=TRUE argument to skip NA elements
sum( allIntakesKCal, na.rm = TRUE )
[1] 14927
- Selecting valid intakes. (Selecting non-missing elements;
logical vectors.)
Understand the result of is.na( allIntakesKCal ).
Now, negate the above result with ! operator.
Use above vectors as argument to sum to calculate the
number of missing and non-missing elements in
allIntakesKCal.
Understand allIntakesKCal[ !is.na( allIntakesKCal ) ].
is.na( allIntakesKCal ) # TRUE marks positions with missing data
[1] FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE
!is.na( allIntakesKCal ) # TRUE marks positions with available data
[1] TRUE TRUE TRUE TRUE TRUE TRUE FALSE TRUE
sum( is.na( allIntakesKCal ) ) # number of missing elements
[1] 1
sum( !is.na( allIntakesKCal ) ) # number of non-missing elements
[1] 7
allIntakesKCal[ !is.na( allIntakesKCal ) ] # keeps elements which are not NA
[1] 2314 2178 1922 2004 2122 2616 1771
sum( allIntakesKCal[ !is.na( allIntakesKCal ) ] ) # same as sum( allIntakesKCal, na.rm = TRUE )
[1] 14927
- Generating random kcal intakes. (Generating normally distributed
random numbers; descriptive statistics.)
The code v <- rnorm( 10 ) would sample 10 numbers from
the normal distribution and store them as a vector in
v.
Print v. Then repeat v <- rnorm( 10 ) and
print v again. Has v changed?
Next, read the manual of rnorm and find how to generate
random numbers with given mean and standard deviation
(sd).
Now, in v simulate kcal intake by generating 15 random
numbers with mean=2000 and sd=300.
Print v and find by eye the smallest and the largest of
these numbers.
Try to use the functions min and max on
v – have you found the same numbers by eye?
Calculate the mean, median and the standard
deviation (sd) of v.
v <- rnorm( 10 ) # a vector of random numbers
v
[1] -0.317453317 -0.278951728 -0.008693089 0.490998379 0.563216354 -1.754787199 1.123204371 1.317944628 -0.567724172 0.559124001
v <- rnorm( 10 ) # another vector of random numbers
v
[1] 0.2422821 1.6005438 0.3573918 0.5359670 1.2018874 -0.3315819 -1.5098508 0.6777799 -0.1116435 0.6504782
v <- rnorm( n = 15, mean = 2000, sd = 300 )
v
[1] 2526.137 1838.740 1347.421 1887.138 2063.207 1925.158 2549.867 1419.239 2228.699 1408.960 2053.324 2166.530 1947.027 2063.544 2054.094
min( v )
[1] 1347.421
max( v )
[1] 2549.867
mean( v ) # is it close to 2000? try several random v vectors and see the effect of growing n
[1] 1965.272
median( v )
[1] 2053.324
sd( v ) # is it close to 300? try several random v vectors and see the effect of growing n
[1] 360.0141
- Selecting and counting some kcal intakes. (Selecting elements by
a condition; logical vectors.)
In v simulate kcal intake by generating 15 random numbers
with mean=2000 and sd=300.
Type v < 2000 and understand the result.
How to interpret the number produced by
sum( v < 2000 )?
How to interpret the number produced by
sum( !( v < 2000 ) )?
v <- rnorm( n = 15, mean = 2000, sd = 300 )
v
[1] 1658.602 2175.611 2503.591 1588.717 2166.811 1681.338 2013.999 2087.824 2359.517 1673.596 1573.656 1383.501 1812.169 2040.121 1598.374
v < 2000 # TRUE corresponds to elements of vector v SMALLER THAN 2000
[1] TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE FALSE TRUE
v[ v < 2000 ] # selected elements of v smaller than 2000
[1] 1658.602 1588.717 1681.338 1673.596 1573.656 1383.501 1812.169 1598.374
sum( v < 2000 ) # number of elements in vector v smaller than 2000
[1] 8
sum( !( v < 2000 ) ) # number of elements in vector v GREATER OR EQUAL than 2000
[1] 7
sum( v >= 2000 ) # same as above
[1] 7
- Head and tail.
Often there is a need to quickly look at the beginning
(head) or at the end (tail) of a vector.
Try these functions to show the first 5 and the last 7 elements of a
randomly generated vector v <- rnorm( 20 ).
v <- rnorm( 20 )
v
[1] -0.217050740 -0.741428154 1.063064538 -0.671520714 -0.317004126 0.385668031 1.148695866 1.578377107 1.172372998 0.006356646 -1.195889002 -0.099010907
[13] 0.419523125 1.884506732 0.020614880 0.384777347 -0.680897339 0.489774408 -0.128292999 0.272453480
head( v, 5 )
[1] -0.2170507 -0.7414282 1.0630645 -0.6715207 -0.3170041
tail( v, 7 )
[1] 1.88450673 0.02061488 0.38477735 -0.68089734 0.48977441 -0.12829300 0.27245348
- Elements of a vector.
Let’s assume that eight persons had caloric intakes of 2122, 2616, NA,
1771, 2314, 2178, 1922, 2004 kcal.
Make a vector intakesKCal of these eight values (in the
given order).
Use the square brackets to get the 4th element of
intakesKCal.
Use the square brackets and the colon operator (:) to get
the elements from the second to the fifth (inclusive).
Define another vector poses with values 1, 3, 5, 7. Use it
get the 1st, 3rd, 5th and 7th element of intakesKCal.
Finally, get the 1st, 3rd, 5th and 7th element of
intakesKCal typing numbers directly inside
[...] (without using an extra poses
variable).
intakesKCal <- c( 2122, 2616, NA, 1771, 2314, 2178, 1922, 2004 )
intakesKCal
[1] 2122 2616 NA 1771 2314 2178 1922 2004
intakesKCal[ 4 ]
[1] 1771
intakesKCal[ 2:5 ]
[1] 2616 NA 1771 2314
poses <- c(1,3,5,7)
intakesKCal[ poses ]
[1] 2122 NA 2314 1922
intakesKCal[ c(1,3,5,7) ]
[1] 2122 NA 2314 1922